Agile Machine Learning

Abstract

All these experiences culminated in the workflow that we are adhering to at work now and that I think is worthwhile sharing. It is heavily based on the principles of Agile software production, hence the title. We have explored which of the concepts from Agile did and did not work for data science and we got hands-on experience in working from these principles in an R project that actually got to production. This text is split into two parts. In the first we will look into the Agile philosophy and some of the methodologies that are closely related to it (chapters 2 and 3). Both will be related to the machine learning context, seeing what we can get from the philosophy (chapter 4) and what an Agile machine learning workflow might look like (chapter 5). The second part is hands on. We will explore how we can leverage the possibilities in the R software system to implement Agile machine learning.